Multi-robot box-pushing in presence of measurement noise

Abstract

Real-world multi-robot co-ordination problems, involving system (robot) design, control, and planning are often formulated in the settings of an optimization problem with a view to maximize system throughput/efficiency under the constraints on system resources. The paper aims at solving a multi-robot box-pushing problem in the presence of noisy sensory data using evolutionary algorithm. The process of co-ordination among multiple robots is characterized by a set of measurements and a set of estimators with a mathematical relationship between them (captured by the objective function). In the box-pushing problem by twin robots, the range data obtained by the robots at any instant of time are measurements, and the torque and/or force to be developed by the robots for a local movement of the box are estimators. We here use torques and forces developed by the robots to construct two objectives on minimization of energy consumed and time required for a local movement of the box in the presence of noisy sensory data. The box-pushing problem is solved using the proposed extended noisy non-dominated sorting bee colony (ENNSBC) algorithm to handle noise in the objective surfaces. Experiments undertaken in both simulation and real-world platforms reveal the superiority of the proposed ENNSBC to other competitor algorithms to solve the box-pushing problem with respect to the performance metrics defined in the literature